Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints
This study investigates using Nonviolent Communication (NVC) principles as lightweight prompt-level constraints to guide large language models (LLMs) toward more de-escalating dialogue behavior in emotionally charged situations. Through a dual-agent simulation framework across multiple models and user resistance levels, NVC-constrained prompting consistently reduces conversational escalation and stabilizes interactions with highly resistant users.
[2606.26106] Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints
[Submitted on 1 May 2026]
Title:Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints
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Abstract:Large language models (LLMs) are increasingly used in emotionally charged situations involving interpersonal conflict, frustration, and distress. While prior safety research has focused on preventing explicit harms such as toxic or policy-violating content, less attention has been paid to conversational behaviors that may unintentionally escalate conflict. In this paper, we investigate whether LLMs can be guided toward more de-escalating dialogue behavior through lightweight prompt-level constraints derived from Nonviolent Communication (NVC). We reformulate NVC principles as process-oriented guidelines that discourage blame attribution, emphasize attention to users' emotional experiences, and encourage clarification before advice. Using a dual-agent simulation framework across multiple instruction-tuned models and user resistance levels, we show that NVC-constrained prompting consistently reduces conversational escalation and stabilizes interactions with highly resistant users. These results suggest that simple communication constraints can meaningfully improve the trustworthiness of LLM dialogue in conflict-prone settings.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.26106 [cs.CL]
(or arXiv:2606.26106v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.26106
arXiv-issued DOI via DataCite
Submission history
From: Tao Li [view email] [v1] Fri, 1 May 2026 16:22:46 UTC (37 KB)
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